Accurately identifying blended galaxies is therefore a key task to guarantee the full potential of the forthcoming cosmological surveys. Several machine learning (ML) based [1]}, [2]} and non-ML based solutions [3]} have been proposed, although the issue remains open. In particular, a robust
quantification of uncertainty to be propagated into the final error budget generally lacks from the current approaches.
